import sys
from pathlib import Path

import torch

from .. import MODEL_REPO_ID, logger

from ..utils.base_model import BaseModel

sys.path.append(str(Path(__file__).parent / "../../third_party"))
from SuperGluePretrainedNetwork.models import superpoint  # noqa E402


# The original keypoint sampling is incorrect. We patch it here but
# we don't fix it upstream to not impact exisiting evaluations.
def sample_descriptors_fix_sampling(keypoints, descriptors, s: int = 8):
    """Interpolate descriptors at keypoint locations"""
    b, c, h, w = descriptors.shape
    keypoints = (keypoints + 0.5) / (keypoints.new_tensor([w, h]) * s)
    keypoints = keypoints * 2 - 1  # normalize to (-1, 1)
    descriptors = torch.nn.functional.grid_sample(
        descriptors,
        keypoints.view(b, 1, -1, 2),
        mode="bilinear",
        align_corners=False,
    )
    descriptors = torch.nn.functional.normalize(
        descriptors.reshape(b, c, -1), p=2, dim=1
    )
    return descriptors


class SuperPoint(BaseModel):
    default_conf = {
        "nms_radius": 4,
        "model_name": "superpoint_v1.pth",
        "keypoint_threshold": 0.005,
        "max_keypoints": -1,
        "remove_borders": 4,
        "fix_sampling": False,
    }
    required_inputs = ["image"]
    detection_noise = 2.0

    def _init(self, conf):
        if conf["fix_sampling"]:
            superpoint.sample_descriptors = sample_descriptors_fix_sampling
        weights_path = self._download_model(
            repo_id=MODEL_REPO_ID,
            filename="{}/{}".format("superglue", self.conf["model_name"]),
        )
        conf["weights_path"] = str(weights_path)
        self.net = superpoint.SuperPoint(conf)
        logger.info("Load SuperPoint model done.")

    def _forward(self, data):
        return self.net(data, self.conf)
